Energy charging and discharging control method and system based on water-light-storage integration

Abstract

The invention provides an energy charging and discharging control method and system based on water-light-storage integration, and relates to the technical field of charging and discharging control, and the method comprises the steps: carrying out the analysis of a historical energy application log through combining with a preset unit period, and generating a historical energy application amount time sequence; obtaining a target energy demand quantity of the target period; introducing a radiation loss correction function to carry out loss analysis on the solar energy output characteristic information to obtain a loss correction result, and obtaining a predicted energy output; judging whether the predicted energy output is greater than the target energy demand or not to obtain a judgment result; and reading a predetermined charging and discharging strategy according to the judgment result to carry out charging and discharging control. By accurately predicting and comparing the predicted energy output quantity and the target energy demand quantity, the energy storage equipment is activated to perform charge and discharge control, supply and demand balance of energy is ensured, and energy utilization is optimized. And the effects of activating the energy storage equipment to carry out charging and discharging control according to the actual situation, ensuring the balance of supply and demand of energy and optimizing energy utilization are achieved.

Description

Technical Field

[0001] The present application relates to the field of charge and discharge control technology, and in particular to an energy charge and discharge control method and system based on integrated water, light and storage. Background Art

[0002] With the continuous growth of global energy demand and the increasing attention to environmental protection, the effective use of renewable energy and energy management have become an important issue. As a clean and renewable energy source, the output of solar energy is affected by many factors, such as weather conditions, photovoltaic panels and time. Therefore, accurately predicting solar energy output and grid energy demand and achieving the energy charging and discharging control goal of integrated water, photovoltaic and storage are of great significance for optimizing energy utilization and reducing costs.

[0003] Energy forecasting and control in existing technologies are usually based on simple statistical models or empirical rules, lacking in-depth analysis of historical data and accurate prediction of future trends. In order to improve the accuracy and efficiency of energy forecasting, a method is needed that can combine historical data and real-time monitoring information to dynamically adjust energy use strategies. Summary of the invention

[0004] The purpose of this application is to provide an energy charging and discharging control method and system based on integrated water, photovoltaic and storage, so as to solve the problem that the existing traditional energy demand forecasting method may not be able to provide sufficiently accurate forecasting results, resulting in low energy management efficiency. At the same time, since solar energy output is affected by many factors, it is impossible to quickly and accurately obtain the solar energy output forecast value. Furthermore, in the absence of accurate prediction and real-time monitoring, energy charging and discharging control may not be able to achieve optimal operation control, ultimately leading to the technical problem of low energy utilization efficiency.

[0005] In view of the above problems, the present application provides an energy charging and discharging control method and system based on integrated water, light and storage.

[0006] In the first aspect, the present application provides an energy charging and discharging control method based on the integration of water, photovoltaic and storage, and the method is implemented by an energy charging and discharging control system based on the integration of water, photovoltaic and storage, wherein the energy charging and discharging control method based on the integration of water, photovoltaic and storage includes: analyzing historical energy application logs in combination with a predetermined unit cycle to generate a historical energy application time series; performing regression fitting analysis on the historical energy application time series to obtain a historical energy application polynomial, and predicting a target energy demand for a target cycle based on the historical energy application polynomial; solar energy output monitoring equipment dynamically monitors based on predetermined output factor dimensions to obtain solar energy output characteristic information; introducing a radiation loss correction function to perform loss analysis on the solar energy output characteristic information to obtain a loss correction result, and analyzing the loss correction result to obtain a predicted energy output; judging whether the predicted energy output is greater than the target energy demand to obtain a judgment result; reading a predetermined charging and discharging strategy based on the judgment result, and activating the energy storage device for charging and discharging control according to the predetermined charging and discharging strategy.

[0007] In the second aspect, the present application also provides an energy charging and discharging control system based on the integration of water, photovoltaic and storage, which is used to execute the energy charging and discharging control method based on the integration of water, photovoltaic and storage as described in the first aspect, wherein the energy charging and discharging control system based on the integration of water, photovoltaic and storage includes: a first generating module, the first generating module is used to analyze the historical energy application log in combination with a predetermined unit period to generate a historical energy application amount time series; a first obtaining module, the first obtaining module is used to perform a regression fitting analysis on the historical energy application amount time series to obtain a historical energy application amount polynomial, and predict the target energy demand for the target period based on the historical energy application amount polynomial; a second obtaining module, the first obtaining module is used to perform a regression fitting analysis on the historical energy application amount time series to obtain a historical energy application amount polynomial, and predict the target energy demand for the target period based on the historical energy application amount polynomial; The second obtaining module is used for the solar energy output monitoring device to dynamically monitor and obtain solar energy output characteristic information based on the predetermined output factor dimension; the third obtaining module, the third obtaining module is used to introduce a radiation loss correction function to perform a loss analysis on the solar energy output characteristic information to obtain a loss correction result, and analyze the loss correction result to obtain a predicted energy output; the fourth obtaining module, the fourth obtaining module is used to determine whether the predicted energy output is greater than the target energy demand, and obtain a judgment result; the first execution module, the first execution module is used to read a predetermined charging and discharging strategy according to the judgment result, and activate the energy storage device for charging and discharging control according to the predetermined charging and discharging strategy.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: By analyzing the historical energy application log in combination with the predetermined unit cycle, the historical energy application amount time series is generated; the historical energy application amount time series is subjected to regression fitting analysis to obtain the historical energy application amount polynomial, and the target energy demand of the target cycle is predicted based on the historical energy application amount polynomial; the solar energy output monitoring device dynamically monitors the solar energy output characteristic information based on the predetermined output factor dimension; the radiation loss correction function is introduced to perform loss analysis on the solar energy output characteristic information to obtain the loss correction result, and the loss correction result is analyzed to obtain the predicted energy output; it is judged whether the predicted energy output is greater than the target energy demand to obtain the judgment result; the predetermined charge and discharge strategy is read according to the judgment result, and the energy storage device is activated according to the predetermined charge and discharge strategy to perform charge and discharge control. That is to say, the time series data of the energy application amount is generated by analyzing the historical energy application log, and then a polynomial is obtained by regression fitting analysis of these data to predict the energy demand of the target cycle. At the same time, the solar energy output monitoring device dynamically monitors the solar energy output characteristic information, and analyzes these information by introducing the radiation loss correction function to obtain the predicted energy output. Finally, by comparing the predicted energy output and the target energy demand, it is decided whether to activate the energy storage device for charge and discharge control, which depends on the predetermined charge and discharge strategy. Through in-depth analysis and regression fitting of historical energy application logs, accurate prediction of future energy demand is achieved. At the same time, the solar output characteristic information obtained by the solar output monitoring equipment is analyzed in combination with the radiation loss correction function to obtain an accurate prediction of solar output. Further, by comparing the predicted energy output and the target energy demand, the energy storage device is activated for charge and discharge control to ensure the balance of energy supply and demand and optimize energy utilization. The technical effect of activating the energy storage device for charge and discharge control according to the actual situation, ensuring the balance of energy supply and demand, and optimizing energy utilization is achieved.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1 This is a flow chart of the energy charging and discharging control method based on the integration of water, light and storage in this application; Figure 2 This is a structural schematic diagram of the energy charging and discharging control system based on the integration of water, light and storage in this application.

[0012] Description of reference numerals: A first generating module 11 , a first obtaining module 12 , a second obtaining module 13 , a third obtaining module 14 , a fourth obtaining module 15 , and a first executing module 16 . DETAILED DESCRIPTION

[0013] This application provides an energy charging and discharging control method and system based on the integration of water, light and storage, which solves the problem that the current energy demand and solar energy output forecasts are not timely and accurate, which further leads to the technical problem that the energy charging and discharging control cannot achieve optimal operation control and is not conducive to the efficient use of energy. It achieves the technical effect of activating energy storage equipment for charging and discharging control according to actual conditions, ensuring the balance of energy supply and demand, and optimizing energy utilization.

[0014] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0015] For example, please refer to the attached Figure 1 The present application provides an energy charging and discharging control method based on water-photovoltaic storage integration, wherein the energy charging and discharging control method based on water-photovoltaic storage integration is applied to an energy charging and discharging control system based on water-photovoltaic storage integration, and the energy charging and discharging control method based on water-photovoltaic storage integration specifically includes the following steps: Step 1: Analyze the historical energy application logs in combination with the predetermined unit period to generate a time series of historical energy application amounts.

[0016] Specifically, by analyzing historical energy application logs in combination with predetermined unit periods to generate a time series of historical energy application amounts, the patterns and trends of energy consumption can be revealed, thereby better understanding and managing energy use.

[0017] First, historical energy application logs need to be collected and organized. These logs usually contain detailed information such as the time, location, and amount of energy used. Then, these logs are segmented according to predetermined unit cycles, such as hourly, daily, or monthly. Next, the energy application amount within each cycle is counted and analyzed to generate time series data of energy usage. In addition, other factors such as weather, season, or specific events can be combined to further analyze the changes and trends in energy use. Specifically, first, a data collection system needs to be established to ensure that energy application logs can be accurately recorded and stored. Then, the collected data is processed and analyzed using data analysis tools such as time series analysis or machine learning algorithms. Next, based on the analysis results, a time series chart of energy application amount is generated to intuitively display the patterns and trends of energy use. In addition, a forecasting function can be included to analyze future energy usage based on historical data, thereby providing a basis for energy management and optimization. Finally, this analysis can provide a more accurate understanding of energy usage, discover potential energy-saving opportunities, and achieve efficient use of energy.

[0018] Step 2: Perform regression fitting analysis on the historical energy application time series to obtain a historical energy application polynomial, and predict the target energy demand of the target period based on the historical energy application polynomial.

[0019] Specifically, a regression fitting analysis is performed on the historical energy application time series to obtain a historical energy application polynomial, and the target energy demand of the target period is predicted based on the polynomial, which helps to improve the accuracy and efficiency of energy management.

[0020] First, collect and organize the time series data of historical energy application. Then, use regression analysis technology to fit these data to find the relationship and trend between the data. Next, based on the fitting results, generate a polynomial function that can describe the law of energy application over time. In addition, other influencing factors such as weather, season or economic activities can also be considered to improve the accuracy of the prediction. In the specific implementation process, first determine the appropriate regression model, such as linear regression, polynomial regression or time series analysis. Then, use historical data to train the model and optimize the model parameters. Next, use the trained model to predict the energy demand for the target period. In addition, in order to improve the accuracy of the prediction, the model can be evaluated and adjusted by methods such as cross-validation. By predicting future energy demand based on historical data, it provides an important reference for energy management and planning.

[0021] Step 3: The solar energy output monitoring device dynamically monitors the solar energy output characteristic information based on the predetermined output factor dimensions.

[0022] Specifically, solar energy output monitoring equipment dynamically monitors the characteristics of solar energy output based on predetermined output factor dimensions, and can understand the performance and efficiency of solar power generation in real time, thereby providing data support for improving the stability and reliability of solar power generation.

[0023] First, it is necessary to install solar output monitoring equipment that can dynamically collect output data from solar panels based on predetermined output factor dimensions, such as time, location, environmental conditions, etc. The collected data is then transmitted to the data processing center through the data acquisition system. Next, the collected data is analyzed using data analysis algorithms to extract characteristic information about solar output. In addition, solar output can be predicted and optimized in combination with weather forecasts and historical data. Dynamic monitoring and analysis can provide real-time insights into the performance and efficiency of solar power generation, identify potential optimization opportunities, and achieve efficient use of solar power generation.

[0024] Step 4: Introduce a radiation loss correction function to perform loss analysis on the solar energy output characteristic information to obtain a loss correction result, and analyze the loss correction result to obtain a predicted energy output.

[0025] Specifically, a radiation loss correction function is introduced to perform loss analysis on the solar energy output characteristic information to obtain the loss correction result, and the loss correction result is analyzed to obtain the predicted energy output. By identifying and quantifying the factors affecting the efficiency of solar panels, such as dust, shadow, temperature, etc., the actual output is corrected to more accurately predict future energy output.

[0026] First, collect the characteristic information of solar energy output, including environmental factors such as light intensity, temperature, and humidity. Then, introduce the radiation loss correction function, which can estimate the energy loss caused by radiation loss based on these characteristic information. Next, use the correction function to analyze the characteristic information of solar energy output to obtain the loss correction result. In addition, historical data and real-time monitoring data can be combined to improve the accuracy of the correction. Through this correction and analysis, the output of solar energy can be predicted more accurately, providing a scientific basis for the planning and scheduling of solar power generation.

[0027] Step 5: Determine whether the predicted energy output is greater than the target energy demand and obtain a determination result.

[0028] Step six: Read the predetermined charge and discharge strategy according to the judgment result, and activate the energy storage device to perform charge and discharge control according to the predetermined charge and discharge strategy.

[0029] Specifically, it is determined whether the predicted energy output is greater than the target energy demand to obtain a judgment result. According to the judgment result, the predetermined charging and discharging strategy is read, and the energy storage device is activated for charging and discharging control according to the predetermined charging and discharging strategy. By comparing the predicted solar energy output with the actual energy demand, it is determined whether the energy storage device needs to be used to balance the supply and demand, thereby improving the flexibility and efficiency of energy utilization.

[0030] First, based on the collected solar energy output characteristic information and historical data, the future energy output is predicted. Then, the predicted energy output is compared with the target energy demand. Next, based on the comparison results, it is determined whether the energy storage device needs to be activated for charging and discharging. In addition, other factors such as grid load and electricity price fluctuations can be considered to optimize the charging and discharging strategy. Through this intelligent control method, the charging and discharging of the energy storage device can be automatically adjusted according to the energy supply and demand, achieving efficient energy utilization and optimized management.

[0031] Further, it includes: the historical energy application time series includes multiple periods with energy application identification; a historical energy application scatter plot is drawn according to the multiple periods with energy application identification; a first scatter point set in the historical energy application scatter plot is obtained by using the principle of random sampling consistency; a first fitting polynomial is performed on the first scatter point set to obtain a first fitting polynomial; a predetermined inspection strategy is read, and a first fitting curve of the first fitting polynomial is inspected and analyzed based on the predetermined inspection strategy to obtain a first inspection result; when the first inspection result satisfies a predetermined inspection constraint, the first fitting polynomial is recorded as the historical energy application polynomial.

[0032] Specifically, useful information is extracted from historical energy application data through data analysis and mathematical modeling to predict future energy demand. First, a scatter plot is drawn based on the historical energy application time series data, and each point represents the energy application amount in a cycle. Then, the first scatter point set is selected from the scatter plot using the principle of random sampling consistency, and the set can represent the characteristics of the overall data. Next, a polynomial fitting is performed on the first scatter point set to obtain a first fitting polynomial, which can describe the changing trend of energy application over time. In addition, in order to ensure the reliability of the fitting results, it is necessary to read the predetermined inspection strategy and perform inspection and analysis on the fitting curve of the first fitting polynomial.

[0033] Specifically, first, it is necessary to determine a suitable random sampling method to ensure the representativeness and accuracy of the selected scattered point set. Then, select a suitable polynomial fitting algorithm, such as the least squares method, to fit the scattered point set. Next, the fitting curve is evaluated according to a predetermined test strategy, such as a goodness of fit test or a hypothesis test. In addition, it is necessary to set test constraints, such as a goodness of fit threshold, to determine whether the fitting result meets the requirements. Through this analysis and testing, a reliable polynomial model can be extracted from historical energy application data to predict future energy demand. This not only helps to improve the accuracy of energy demand forecasts, but also helps to optimize energy management and scheduling, and promote efficient use and sustainable development of energy.

[0034] Further, it includes: obtaining a first inspection scatter point set based on the predetermined inspection strategy, wherein the first inspection scatter point set refers to a set of scatter points after the first scatter point set is removed from the historical energy application scatter plot; obtaining a first spatial distance from the first inspection scatter point to the first fitting curve, wherein the first inspection scatter point refers to any scatter point in the first inspection scatter point set; judging whether the first spatial distance is within a predetermined distance threshold; if so, adding the first inspection scatter point to the first inner point set, and if not, adding the first inspection scatter point to the first outer point set; calculating the ratio of the number of scatter points in the first inner point set to the number of scatter points in the first outer point set, recorded as a first inspection ratio; and recording the first inspection ratio as the first inspection result.

[0035] Specifically, the accuracy and reliability of the polynomial fitting model are evaluated through mathematical statistics and geometric analysis. First, according to a predetermined inspection strategy, the first scattered point set is removed from the historical energy application scatter plot to form a first inspection scattered point set. Then, the spatial distance from each scattered point in the first inspection scatter point set to the first fitting curve is calculated. Next, according to a predetermined distance threshold, it is judged whether the spatial distance of each scattered point is within an acceptable range. In addition, according to the judgment result, the scattered points are divided into an inner point set and an outer point set, representing data points with good and bad fitting effects, respectively. Specifically, first, it is necessary to determine a suitable inspection strategy and distance threshold. Then, the spatial distance from the scattered points to the fitting curve is calculated using a geometric analysis method. Next, the scattered points are classified according to the distance threshold. In addition, it is necessary to calculate the number of scattered points in the inner point set and the outer point set to obtain a first inspection ratio. Through this analysis and calculation, the first inspection result, that is, the ratio of the number of scattered points in the inner point set to the number of scattered points in the outer point set, can be obtained, and the ratio can be used as an indicator for evaluating the accuracy of the polynomial fitting model. This not only helps to evaluate the effectiveness of the model, but also helps to discover possible deviations and errors in the model, thereby improving the accuracy and reliability of the model prediction.

[0036] Further, it includes: the solar energy output monitoring equipment includes a radiation monitoring component, a weather monitoring component and a photovoltaic panel monitoring component; the solar energy radiation intensity record is obtained by dynamic monitoring of the radiation monitoring component; the weather characteristic parameter record is obtained by dynamic monitoring of the weather monitoring component, and the weather characteristic parameter record includes a cloud cover record, a temperature record and a humidity record; the photovoltaic panel characteristic information is obtained by monitoring of the photovoltaic panel monitoring component, and the photovoltaic panel characteristic information includes a photovoltaic conversion efficiency and a photovoltaic angle deviation record; the solar energy radiation intensity record, the cloud cover record, the temperature record, the humidity record, the photovoltaic conversion efficiency and the photovoltaic angle deviation record constitute the solar energy output characteristic information.

[0037] Specifically, through real-time monitoring and multi-dimensional data collection, a comprehensive understanding of the performance and efficiency of the solar power generation system is achieved. First, the radiation monitoring component is used to collect data on solar radiation intensity, which is a key factor in solar energy output. Then, the weather monitoring component is used to obtain weather characteristic parameters such as cloudiness, temperature and humidity, which will affect the actual output of solar panels. Next, the photovoltaic panel monitoring component is used to collect characteristic information of photovoltaic panels, such as photovoltaic conversion efficiency and angle deviation, which is crucial for evaluating the working status and performance of photovoltaic panels. In addition, these records are integrated together to form solar energy output characteristic information, providing comprehensive data support for subsequent data analysis and optimization.

[0038] Specifically, it is necessary to first ensure the accuracy and stability of the monitoring equipment to obtain high-quality monitoring data. Then, the collected data is transmitted to the data processing center using the data acquisition system. Next, the data is processed using data analysis algorithms to extract key features. In addition, data visualization tools can be included to display the analysis results in the form of charts or reports for easy user understanding and decision-making. Through this comprehensive monitoring and data collection, the performance and efficiency of the solar power generation system can be understood in real time, potential optimization opportunities can be discovered, and efficient use of solar power generation can be achieved.

[0039] Further, it includes: obtaining a first period, and matching a first radiation intensity parameter of the first period in the solar radiation intensity record to obtain a first average radiation intensity; matching the first cloud cover parameter, the first temperature parameter and the first humidity parameter of the first period in the cloud cover record, the temperature record and the humidity record in sequence; calculating and obtaining a first average cloud cover of the first cloud cover parameter, a first average temperature of the first temperature parameter and a first average humidity of the first humidity parameter in sequence; performing loss correction on the first average cloud cover, the first average temperature, the first average humidity and the first average radiation intensity according to the radiation loss correction function to obtain a first target average radiation intensity; recording the first target average radiation intensity as a first loss correction result of the first period, and composing the loss correction result based on the first loss correction result.

[0040] Specifically, the accuracy of solar energy output prediction is improved through data analysis and correction. First, the first cycle is determined, and the radiation intensity data of the cycle is extracted from the solar radiation intensity record to calculate the first average radiation intensity. Then, the data of the first cycle is extracted from the cloud cover record, the temperature record and the humidity record in turn, and the first average cloud cover, the first average temperature and the first average humidity are calculated. Next, the radiation loss correction function is used to correct these average parameters to estimate the radiation loss caused by environmental factors. In addition, the corrected first target average radiation intensity is used as the loss correction result of the first cycle to provide a more accurate data basis for subsequent energy output prediction. In the specific implementation process, it is first necessary to determine the length and starting point of the cycle. Then, the average value of each parameter is calculated using data analysis techniques, such as statistical methods. Next, the radiation loss correction function is applied, which can estimate and correct the radiation loss based on factors such as cloud cover, temperature, humidity and radiation intensity. In addition, it is necessary to record and store the loss correction results of each cycle for subsequent analysis and prediction. Through this correction and analysis, a more accurate solar energy output prediction can be obtained, thereby improving the efficiency and economy of solar power generation.

[0041] Further, including: the expression of the radiation loss correction function is as follows: ;in, Characterizing the First Cycle The first average radiation intensity under The loss correction result, that is, the average radiation intensity of the first target, Characterizing the First Cycle The next The mean of the weather factor characteristics, Characterizing the The weather factor characteristics have an impact on the first average radiation intensity The loss impact coefficient of .

[0042] Specifically, the expression of the radiation loss correction function is as follows: ;in, Characterizing the First Cycle The first average radiation intensity under The loss correction result, that is, the average radiation intensity of the first target, Characterizing the First Cycle The next The mean of the weather factor characteristics, Characterizing the The weather factor characteristics have an impact on the first average radiation intensity The loss impact coefficient of , namely the three weather indicators of cloud cover, temperature and humidity.

[0043] Further, it includes: obtaining a predetermined correlation coefficient, and combining the predetermined correlation coefficient with the first target average radiation intensity to obtain a first predicted energy output; adding the first predicted energy output to obtain the predicted energy output.

[0044] Specifically, the output of the solar power generation system is predicted through correlation analysis and mathematical modeling, thereby providing a basis for energy management and optimization. First, a predetermined correlation coefficient is determined, which reflects the correlation between solar radiation intensity and energy output. Then, the first predicted energy output is calculated by mathematical model using this correlation coefficient and the first target average radiation intensity. Next, the first predicted energy output is added to the predicted output of other periods to obtain the total predicted energy output. In addition, this method can also consider other factors, such as weather changes, seasonal effects, etc., to improve the accuracy of the prediction. In the specific implementation process, it is first necessary to determine the correlation coefficient based on historical data, which can be achieved through statistical analysis and data mining technology. Then, the predicted energy output is calculated by mathematical formula or model using the correlation coefficient and the target average radiation intensity. Next, the predicted output of all periods is added to obtain the total predicted energy output. In addition, in order to improve the accuracy of the prediction, the correlation coefficient can also be continuously updated and optimized to adapt to environmental changes and changes in system performance. Through this correlation analysis and prediction, the total output of the solar power generation system in the future period can be obtained, providing an important reference for energy management and scheduling.

[0045] Further, it includes: according to the judgment result, when the predicted energy output is greater than the target energy demand, activating the energy storage device to perform charging control of pumped storage. According to the judgment result, when the predicted energy output is less than the target energy demand, activating the energy storage device to perform discharging control of releasing water and electricity.

[0046] Specifically, by intelligently controlling the charging and discharging process of energy storage equipment, the balance of energy supply and demand is achieved, and the flexibility and efficiency of energy utilization are improved. First, the predicted energy output and the target energy demand are compared. Then, according to the comparison results, when the predicted output is greater than the demand, the energy storage equipment is activated for charging to store the excess energy. Next, when the predicted output is less than the demand, the energy storage equipment is activated for discharge to release the stored energy to meet the demand. In the specific implementation process, it is first necessary to establish an energy demand forecasting model to accurately predict the energy demand of the target cycle. Then, the forecast results are evaluated using data analysis techniques, such as machine learning algorithms. Next, according to the evaluation results, a suitable charging and discharging strategy is selected. In addition, an intelligent control system is required to automatically control the charging and discharging process of the energy storage equipment according to the selected strategy. Through this intelligent control method, the charging and discharging of the energy storage equipment can be automatically adjusted according to the energy supply and demand, and efficient utilization and optimized management of energy can be achieved.

[0047] In summary, the energy charging and discharging control method based on water-photovoltaic-storage integration provided in this application has the following technical effects: By analyzing the historical energy application log in combination with the predetermined unit cycle, the historical energy application amount time series is generated; the historical energy application amount time series is subjected to regression fitting analysis to obtain the historical energy application amount polynomial, and the target energy demand of the target cycle is predicted based on the historical energy application amount polynomial; the solar energy output monitoring device dynamically monitors the solar energy output characteristic information based on the predetermined output factor dimension; the radiation loss correction function is introduced to perform loss analysis on the solar energy output characteristic information to obtain the loss correction result, and the loss correction result is analyzed to obtain the predicted energy output; it is judged whether the predicted energy output is greater than the target energy demand to obtain the judgment result; the predetermined charge and discharge strategy is read according to the judgment result, and the energy storage device is activated according to the predetermined charge and discharge strategy to perform charge and discharge control. That is to say, the time series data of the energy application amount is generated by analyzing the historical energy application log, and then a polynomial is obtained by regression fitting analysis of these data to predict the energy demand of the target cycle. At the same time, the solar energy output monitoring device dynamically monitors the solar energy output characteristic information, and analyzes these information by introducing the radiation loss correction function to obtain the predicted energy output. Finally, by comparing the predicted energy output and the target energy demand, it is decided whether to activate the energy storage device for charge and discharge control, which depends on the predetermined charge and discharge strategy. Through in-depth analysis and regression fitting of historical energy application logs, accurate prediction of future energy demand is achieved. At the same time, the solar output characteristic information obtained by the solar output monitoring equipment is analyzed in combination with the radiation loss correction function to obtain an accurate prediction of solar output. Further, by comparing the predicted energy output and the target energy demand, the energy storage device is activated for charge and discharge control to ensure the balance of energy supply and demand and optimize energy utilization. The technical effect of activating the energy storage device for charge and discharge control according to the actual situation, ensuring the balance of energy supply and demand, and optimizing energy utilization is achieved.

[0048] Embodiment 2, based on the same inventive concept as the energy charging and discharging control method based on water-light storage integration in the previous embodiment, this application also provides an energy charging and discharging control system based on water-light storage integration, please refer to the attached Figure 2 , the energy charging and discharging control system based on water-photovoltaic-storage integration includes: A first generating module 11, the first generating module 11 is used to analyze the historical energy application log in combination with a predetermined unit period to generate a historical energy application amount time series; a first obtaining module 12, the first obtaining module 12 is used to perform a regression fitting analysis on the historical energy application amount time series to obtain a historical energy application amount polynomial, and predict the target energy demand of the target period based on the historical energy application amount polynomial; a second obtaining module 13, the second obtaining module 13 is used for the solar energy output monitoring device to dynamically monitor the solar energy output characteristic information based on the predetermined output factor dimension; a third obtaining module 14, the third obtaining module 14 is used to introduce a radiation loss correction function to perform a loss analysis on the solar energy output characteristic information to obtain a loss correction result, and analyze the loss correction result to obtain a predicted energy output; a fourth obtaining module 15, the fourth obtaining module 15 is used to determine whether the predicted energy output is greater than the target energy demand, and obtain a judgment result; a first execution module 16, the first execution module 16 is used to read a predetermined charge and discharge strategy according to the judgment result, and activate the energy storage device for charge and discharge control according to the predetermined charge and discharge strategy.

[0049] Furthermore, the first obtaining module 12 in the energy charging and discharging control system based on integrated water, photovoltaic and storage is also used for: the historical energy application time series includes multiple periods with energy application identification; a historical energy application scatter plot is drawn according to the multiple periods with energy application identification; a first scatter point set in the historical energy application scatter plot is obtained by using the principle of random sampling consistency; a first fitting polynomial is performed on the first scatter point set to obtain a first fitting polynomial; a predetermined inspection strategy is read, and a first fitting curve of the first fitting polynomial is inspected and analyzed based on the predetermined inspection strategy to obtain a first inspection result; when the first inspection result satisfies a predetermined inspection constraint, the first fitting polynomial is recorded as the historical energy application polynomial.

[0050] Furthermore, the first obtaining module 12 in the energy charging and discharging control system based on integrated water, photovoltaic and storage is also used to: obtain a first inspection scatter point set based on the predetermined inspection strategy, wherein the first inspection scatter point set refers to a set of scatter points after the first scatter point set is removed from the historical energy application scatter point diagram; obtain a first spatial distance from the first inspection scatter point to the first fitting curve, the first inspection scatter point refers to any scatter point in the first inspection scatter point set; determine whether the first spatial distance is within a predetermined distance threshold; if so, add the first inspection scatter point to the first inner point set, and if not, add the first inspection scatter point to the first outer point set; calculate the ratio of the number of scatter points in the first inner point set to the number of scatter points in the first outer point set, recorded as the first inspection ratio; and record the first inspection ratio as the first inspection result.

[0051] Furthermore, the second obtaining module 13 in the energy charging and discharging control system based on integrated water, photovoltaic and storage is also used for: the solar energy output monitoring equipment includes a radiation monitoring component, a weather monitoring component and a photovoltaic panel monitoring component; the solar energy radiation intensity record is obtained by dynamic monitoring of the radiation monitoring component; the weather characteristic parameter record is obtained by dynamic monitoring of the weather monitoring component, and the weather characteristic parameter record includes a cloud cover record, a temperature record and a humidity record; the photovoltaic panel characteristic information is obtained by monitoring the photovoltaic panel monitoring component, and the photovoltaic panel characteristic information includes a photovoltaic conversion efficiency and a photovoltaic angle deviation record; the solar energy radiation intensity record, the cloud cover record, the temperature record, the humidity record, the photovoltaic conversion efficiency and the photovoltaic angle deviation record constitute the solar energy output characteristic information.

[0052] Furthermore, the third obtaining module 14 in the energy charging and discharging control system based on integrated water, photovoltaic and storage is also used to: obtain the first period, and match the first radiation intensity parameter of the first period in the solar radiation intensity record to obtain the first average radiation intensity; sequentially match the cloud cover record, the temperature record and the humidity record to obtain the first cloud cover parameter, the first temperature parameter and the first humidity parameter of the first period; sequentially calculate and obtain the first average cloud cover of the first cloud cover parameter, the first average temperature of the first temperature parameter and the first average humidity of the first humidity parameter; perform loss correction on the first average cloud cover, the first average temperature, the first average humidity and the first average radiation intensity according to the radiation loss correction function to obtain the first target average radiation intensity; record the first target average radiation intensity as the first loss correction result of the first period, and form the loss correction result based on the first loss correction result.

[0053] Furthermore, the third obtaining module 14 in the energy charging and discharging control system based on water-photovoltaic-storage integration is also used for: the expression of the radiation loss correction function is as follows: ;in, Characterizing the First Cycle The first average radiation intensity under The loss correction result, that is, the average radiation intensity of the first target, Characterizing the First Cycle The next The mean of the weather factor characteristics, Characterizing the The weather factor characteristics have an impact on the first average radiation intensity The loss impact coefficient of .

[0054] Furthermore, the third obtaining module 14 in the energy charging and discharging control system based on integrated water, photovoltaic and storage is also used to: obtain a predetermined correlation coefficient, and combine the predetermined correlation coefficient with the first target average radiation intensity to obtain a first predicted energy output; add the first predicted energy output to obtain the predicted energy output.

[0055] Furthermore, the first execution module 16 in the energy charging and discharging control system based on integrated water, photovoltaic and storage is also used to: according to the judgment result, when the predicted energy output is greater than the target energy demand, activate the energy storage equipment to perform pumped storage charging control.

[0056] Furthermore, the first execution module 16 in the energy charging and discharging control system based on integrated water, photovoltaic and storage is also used to: according to the judgment result, when the predicted energy output is less than the target energy demand, activate the energy storage device to perform discharge control for releasing water and electricity.

[0057] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The energy charging and discharging control method based on water-light storage integration and the specific examples in Example 1 are also applicable to the energy charging and discharging control system based on water-light storage integration in this embodiment. Through the above detailed description of the energy charging and discharging control method based on water-light storage integration, those skilled in the art can clearly know the energy charging and discharging control system based on water-light storage integration in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0058] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0059] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. An energy charging and discharging control method based on water-photovoltaic-storage integration is characterized in that: include: Analyze historical energy application logs in combination with a predetermined unit period to generate a time series of historical energy application amounts; Performing regression fitting analysis on the historical energy application time series to obtain a historical energy application polynomial, and predicting a target energy demand for a target period based on the historical energy application polynomial; The solar energy output monitoring equipment dynamically monitors the solar energy output characteristic information based on the predetermined output factor dimensions; Introducing a radiation loss correction function to perform loss analysis on the solar energy output characteristic information to obtain a loss correction result, and analyzing the loss correction result to obtain a predicted energy output; Determine whether the predicted energy output is greater than the target energy demand, and obtain a determination result; The predetermined charging and discharging strategy is read according to the judgment result, and the energy storage device is activated to perform charging and discharging control according to the predetermined charging and discharging strategy.

2. According to claim 1, the energy charging and discharging control method based on water-photovoltaic-storage integration is characterized in that: include: The historical energy usage time series includes a plurality of periods with energy usage identifiers; Draw a scatter plot of historical energy usage according to the multiple periods with energy usage identifiers; Obtaining a first scatter point set in the historical energy application amount scatter diagram by using a random sampling consistency principle; Performing polynomial fitting on the first scattered point set to obtain a first fitting polynomial; Reading a predetermined inspection strategy, and performing inspection and analysis on a first fitting curve of the first fitting polynomial based on the predetermined inspection strategy to obtain a first inspection result; When the first inspection result satisfies a predetermined inspection constraint, the first fitting polynomial is recorded as the historical energy application amount polynomial.

3. According to claim 2, the energy charging and discharging control method based on water-photovoltaic-storage integration is characterized in that: include: Based on the predetermined inspection strategy, a first inspection scatter point set is obtained, wherein the first inspection scatter point set refers to a set of scatter points after the first scatter point set is removed from the historical energy application amount scatter point graph; Obtaining a first spatial distance from a first inspection scatter point to the first fitting curve, where the first inspection scatter point refers to any scatter point in the first inspection scatter point set; Determining whether the first spatial distance is within a predetermined distance threshold; If it is, add the first inspection scattered point to the first internal point set; if it is not, add the first inspection scattered point to the first external point set; Calculate the ratio of the number of scattered points in the first internal point set to the number of scattered points in the first external point set, and record it as a first test ratio; The first test ratio is recorded as the first test result.

4. According to claim 1, the energy charging and discharging control method based on water-photovoltaic-storage integration is characterized in that: include: The solar energy output monitoring equipment includes a radiation monitoring component, a weather monitoring component and a photovoltaic panel monitoring component; Dynamically monitor and obtain solar radiation intensity records through the radiation monitoring component; Dynamically monitor and obtain weather characteristic parameter records through the weather monitoring component, wherein the weather characteristic parameter records include cloud cover records, temperature records and humidity records; The photovoltaic panel characteristic information is obtained by monitoring the photovoltaic panel monitoring component, wherein the photovoltaic panel characteristic information includes photovoltaic conversion efficiency and photovoltaic angle deviation records; The solar radiation intensity record, the cloud cover record, the temperature record, the humidity record, the photovoltaic conversion efficiency and the photovoltaic angle deviation record constitute the solar energy output characteristic information.

5. The energy charging and discharging control method based on water-photovoltaic-storage integration according to claim 4 is characterized in that: include: Acquire a first cycle, and match a first radiation intensity parameter of the first cycle in the solar radiation intensity record to obtain a first average radiation intensity; The cloud cover record, the temperature record and the humidity record are matched in sequence to obtain a first cloud cover parameter, a first temperature parameter and a first humidity parameter of the first period; sequentially calculating and acquiring a first average cloud amount of the first cloud amount parameter, a first average temperature of the first temperature parameter, and a first average humidity of the first humidity parameter; Perform loss correction on the first average cloud amount, the first average temperature, the first average humidity and the first average radiation intensity according to the radiation loss correction function to obtain a first target average radiation intensity; The first target average radiation intensity is recorded as a first loss correction result of the first period, and the loss correction result is composed based on the first loss correction result.

6. The energy charging and discharging control method based on water-photovoltaic-storage integration according to claim 5 is characterized in that: The expression of the radiation loss correction function is as follows: ； in, Characterizing the First Cycle The first average radiation intensity under The loss correction result, that is, the average radiation intensity of the first target, Characterizing the First Cycle The next The mean of the weather factor characteristics, Characterizing the The weather factor characteristics have an impact on the first average radiation intensity The loss impact coefficient of .

7. The energy charging and discharging control method based on water-photovoltaic-storage integration according to claim 5 is characterized in that: include: Obtaining a predetermined correlation coefficient, and combining the predetermined correlation coefficient with the first target average radiation intensity to obtain a first predicted energy output; The first predicted energy output is added to obtain the predicted energy output.

8. The energy charging and discharging control method based on water-photovoltaic-storage integration according to claim 1 is characterized in that: include: According to the judgment result, when the predicted energy output is greater than the target energy demand, the energy storage device is activated to perform charging control of pumped storage power.

9. The energy charging and discharging control method based on water-photovoltaic-storage integration according to claim 1 is characterized in that: include: According to the judgment result, when the predicted energy output is less than the target energy demand, the energy storage device is activated to perform discharge control for releasing water and electricity.

10. An energy charging and discharging control system based on water-light-storage integration is characterized by: The steps for implementing the energy charging and discharging control method based on water-photovoltaic storage integration as described in any one of claims 1 to 9, the energy charging and discharging control system based on water-photovoltaic storage integration comprises: A first generating module, the first generating module is used to analyze the historical energy application log in combination with a predetermined unit period to generate a historical energy application amount time series; A first obtaining module, wherein the first obtaining module is used to perform regression fitting analysis on the historical energy application amount time series to obtain a historical energy application amount polynomial, and predict a target energy demand amount for a target period according to the historical energy application amount polynomial; A second obtaining module, wherein the second obtaining module is used for the solar energy output monitoring device to dynamically monitor and obtain solar energy output characteristic information based on a predetermined output factor dimension; a third obtaining module, the third obtaining module is used to introduce a radiation loss correction function to perform loss analysis on the solar energy output characteristic information to obtain a loss correction result, and analyze the loss correction result to obtain a predicted energy output; A fourth obtaining module, the fourth obtaining module is used to determine whether the predicted energy output is greater than the target energy demand, and obtain a determination result; The first execution module is used to read a predetermined charging and discharging strategy according to the judgment result, and activate the energy storage device to perform charging and discharging control according to the predetermined charging and discharging strategy.

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